Recent years have witnessed great success in applying deep convolutional neural networks (CNNs) on video frame interpolation (VFI). The existing approaches for VFI can be divided into three categories: phase-based methods (e.g., [1]}), kernel-based methods (e.g., [2]}), and flow-based methods (e.g., [3]}, [4]}, [5]}). It can be observed that flow-based approaches perform well in quantitative benchmarks [6]}. Among them, the work DAIN [3]} via the advanced flow estimation method PWC-Net [8]}, delivers the state-of-the-art performance. It is highly desirable to bring this technology to point cloud frame interpolation. However, it is not a direct extension. That is because point clouds have the characteristics: with the high dimensionality (i.e., 3D), huge amounts of data and the unstructured nature, imposing restrictions on adopting well-developed VFI methods (typically using 2D CNNs) directly.
